MIT Researchers More Accurately Study Sleep

Researchers at MIT have begun using radio signals and artificial intelligence algorithms to analyze patients' sleep stages without physical sensors which is a very accurate method.

This way could help people with Parkinson's, Alzheimer's and epilepsy, all of whom can have sleep disruptions that are hard to detect, Engadget reports. While the MIT system is in its infancy now, it's easy to imagine a near future with home-based sleep monitoring using radio frequencies (RF).

"Imagine if your Wi-Fi router knows when you are dreaming, and can monitor whether you are having enough deep sleep, which is necessary for memory consolidation," said study leader Dina Katabi in a statement. "Our vision is developing health sensors that will disappear into the background and capture physiological signals and important health metrics, without asking the user to change her behavior in any way."

Based on the study, this is the first study that claims a high rate of accuracy (80 percent) as measured against EEG recordings. The RF signals gather some irrelevant information when tracking sleep, so the MIT team had to come up with new algorithms to help separate out the important data. The new sleep monitoring system uses deep neural networks and unique, MIT-written AI algorithms to analyze the data to translate the raw information to valuable sleep data. The team plans to use this new technique to study how Parkinson's affects sleep next.

The current sleep monitor builds on previous radio-based systems the team has created that use low-power RF signals to detect and analyze emotions via vital signs like pulse and respiration. They've also used RF to measure walking speed, which can help doctors predict cognitive decline, falls and some cardiac or pulmonary diseases.